Blumer Ofir, Reuveni Shlomi, Hirshberg Barak
School of Chemistry, Tel Aviv University, Tel Aviv 6997801, Israel.
The Center for Computational Molecular and Materials Science, Tel Aviv University, Tel Aviv 6997801, Israel.
J Chem Phys. 2024 Dec 14;161(22). doi: 10.1063/5.0243783.
We present an inference scheme of long timescale, non-exponential kinetics from molecular dynamics simulations accelerated by stochastic resetting. Standard simulations provide valuable insight into chemical processes but are limited to timescales shorter than ∼1μs. Slower processes require the use of enhanced sampling methods to expedite them and inference schemes to obtain the unbiased kinetics. However, most kinetics inference schemes assume an underlying exponential first-passage time distribution and are inappropriate for other distributions, e.g., with a power-law decay. We propose an inference scheme that is designed for such cases, based on simulations enhanced by stochastic resetting. We show that resetting promotes enhanced sampling of the first-passage time distribution at short timescales but often also provides sufficient information to estimate the long-time asymptotics, which allows the kinetics inference. We apply our method to a model system and a peptide in an explicit solvent, successfully estimating the unbiased mean first-passage time while accelerating the sampling by more than an order of magnitude.
我们提出了一种基于随机重置加速分子动力学模拟的长时非指数动力学推断方案。标准模拟为化学过程提供了有价值的见解，但仅限于短于约1微秒的时间尺度。较慢的过程需要使用增强采样方法来加速，并使用推断方案来获得无偏动力学。然而，大多数动力学推断方案都假设潜在的指数首次通过时间分布，不适用于其他分布，例如具有幂律衰减的分布。我们基于随机重置增强的模拟，提出了一种针对此类情况设计的推断方案。我们表明，重置在短时间尺度上促进了首次通过时间分布的增强采样，但通常也提供了足够的信息来估计长时间渐近性，从而实现动力学推断。我们将我们的方法应用于模型系统和明确溶剂中的肽，成功地估计了无偏平均首次通过时间，同时将采样加速了一个多数量级。
